Forecasting Soil Moisture Using PSO-CNN - LSTM Model

In recent years, the Sustainable Development Goals (SDGs) have received increasing attention from scholars, an international agenda that aims to address global challenges such as poverty, hunger, and environmental damage. By accurately predicting soil moisture, we can effectively address the challenges of food security, sustainable management of water resources, conservation of ecosystems, and response to climate change. By improving soil moisture prediction techniques and methods, these global challenges can be solved more effectively. To better predict soil moisture at different levels, a new PSO-CNN-LSTM is proposed. This model combines the advantages of CNN-LSTM in extracting multivariate feature sequences and PSO in hyper-parametric global optimization. RNN, GRU, LSTM, CNN-GRU and CNN-LSTM are trained and tested as a control group on the publicly available dataset EARS-Land. The results demonstrated the better performance of PSO-CNN-LSTM in predicting soil moisture at different levels with different time output steps. Our study demonstrated that the proposed model could be a promising alternative for predicting soil moisture.